Postgraduate study
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Centre for Doctoral Training in Artificial Intelligence for Biomedical Innovation PhD

Awards: PhD

Study modes: Full-time, Part-time


The greatest challenge to realising the potential of artificial intelligence in the biomedical domain is its translation into real-world use. This programme will train graduates into a workforce with skills in computational and digital skills, and the integration of clinical, genomic, and phenotypic data.

Researchers on this programme will develop technical and domain specific inter-disciplinary research skills, and gain experience delivering innovation into the public and private sectors. They will learn to successfully design, develop, and implement AI approaches in partnership with external stakeholders.

Our programme is especially suitable for those with relatively little prior exposure to computer science and mathematics. This includes clinicians, allied health professionals, and biological/biomedical scientists. Our graduates will acquire technical skills from computer science, mathematics, and statistics, and domain knowledge from biomedical and clinical sciences. They will know how to practice responsible research innovation, adopt the best practice for minimising the risk of bias in AI, and understand the importance of model explainability for clinical use.

The research programme is organised into four thematic areas: AI for Innovation in; Biomedical Imaging, Biomedical Engineering, Biomedical & Health Informatics, and Genomic Medicine each with an expert theme leader supported by groups of at least twenty project supervisors from across the University.

Our training is organised into overlapping academic areas: Artificial Intelligence, Biomedical, and Health each with an expert training lead and dedicated cross-programme training leads in RRI/Ethics, Entrepreneurship, and an ED&I Champion. These roles help to visualise the breadth and depth of the programme, tackling research challenges of profound societal and economic importance with an ethical and inclusive research ethos.

Our programme is a 4 year PhD with integrated study in which students will take 180 credits of courses spread over years 1-3 whilst undertaking their PhD project research. They will undertake a Placement Dissertation Project (60 credits) for 3 months with either a public or private sector external partner flexibly in any one of years 1-3.

Students will also take three compulsory courses:

  • Year 1: Foundations in Biomedical AI Research (30 credits)
  • Year 2: Interdisciplinary AI Research (20 credits) and Case Studies in AI Ethics (10 credits)

The remaining 60 credits of courses will be selected by students to best suit their learning, research, and career development needs in consultation with their PhD supervisory team and CDT year group mentor.

Students will undertake a “Placement Dissertation Project”, a 3-month course in which the student undertakes a separate small research project in collaboration with an external partner to experience another environment and/or research challenge/technical area during their studies. This may take place remotely with the partner organisation but could also be conducted from the University if those arrangements better suit the student and partner organisation.

The exact profile of the taught component and timing of the placement dissertation project will be formulated in consultation between the individual student, supervisor team and the CDT. Many students will also choose to undertake separate internships during their studies.

Our students will gain an understanding of key challenges and opportunities for the application of AI in Clinical, Biomedical, and Public Health settings. They will gain the skills, knowledge, and experience to develop and implement AI solutions in interdisciplinary research environments and practice responsible research innovation.

  • Teaching and learning methods will include traditional lectures, tutorials, and workshops in the various courses as well as masterclasses, hackathons, and group mini-projects.
  • Training will be delivered by Edinburgh staff, invited lecturers, staff from industry and external partners, facilities/service staff.
  • Entrepreneurship training through “PhD Max”, “Venture Builder Incubator”, and other bespoke training through the Bayes Centre Entrepreneurship team and the programme’s Innovation training lead.
  • Outreach and Public Communication Training from specialist external providers including the Alan Turing Institute.
  • Public Patient Involvement and Engagement training and working with stakeholders to ensure research is well targeted and developed with the interests of all parties considered.
  • Responsible Research & Innovation training in collaboration with the Alan Turing Institute & experts from the University and the CDT’s external partners.

Students will be supported by an experienced team of academic and professional service staff. Each year group will have a dedicated mentor who will work closely with our research and training leads, the CDT management group, external advisory board, and partners to support students.

Students will be embedded in the vibrant world-class and interdisciplinary research community within the Informatics Forum and Bayes Centre with access to state-of-the-art computational infrastructure through the School of Informatics and Edinburgh Parallel Computing Centre. This includes access to large CPU and GPU cluster compute in the Edinburgh International Data Facility.

Graduates with inter-disciplinary skills and experience at the interface between biomedical and computational science are in huge demand, with a well identified skills shortage in the UK and globally. Students will have the opportunity to work directly and learn from external partners in the public and private sectors and will receive bespoke co-delivered career training in the programme. This forms part of a longitudinal training programme that has been developed as part of the CDT in Biomedical AI and is currently being delivered to 4 other programmes at the University.

  • To train PhD students to use machine learning approaches to address real-world problems in inter-disciplinary biomedical, clinical, and health research settings
  • To offer a broad range of co-designed state-of-the-art PhD research projects with external partners in the public and private sector
  • To develop the innovation and entrepreneurship skills of our students through bespoke training programmes co-delivered with the Bayes Centre innovation team
  • To establish ethical, reproducible, and open research practices among our students
  • To provide experience of conducting research in external organisations through 3-month placement projects
  • To enable participation in the programme by students from diverse academic and cultural backgrounds

These entry requirements are for the 2024/25 academic year and requirements for future academic years may differ. Entry requirements for the 2025/26 academic year will be published on 1 Oct 2024.

A UK 2:1 honours degree, or its international equivalent, in an area related to the topic of the CDT, for example, computer science, AI, cognitive science, mathematics, physics, engineering, biomedical science, biological science, and clinical & public health sciences.

International qualifications

Check whether your international qualifications meet our general entry requirements:

English language requirements

Regardless of your nationality or country of residence, you must demonstrate a level of English language competency at a level that will enable you to succeed in your studies.

English language tests

We accept the following English language qualifications at the grades specified:

  • IELTS Academic: total 6.5 with at least 6.0 in each component. We do not accept IELTS One Skill Retake to meet our English language requirements.
  • TOEFL-iBT (including Home Edition): total 92 with at least 20 in each component. We do not accept TOEFL MyBest Score to meet our English language requirements.
  • C1 Advanced (CAE) / C2 Proficiency (CPE): total 176 with at least 169 in each component.
  • Trinity ISE: ISE II with distinctions in all four components.
  • PTE Academic: total 62 with at least 59 in each component.

Your English language qualification must be no more than three and a half years old from the start date of the programme you are applying to study, unless you are using IELTS, TOEFL, Trinity ISE or PTE, in which case it must be no more than two years old.

Degrees taught and assessed in English

We also accept an undergraduate or postgraduate degree that has been taught and assessed in English in a majority English speaking country, as defined by UK Visas and Immigration:

We also accept a degree that has been taught and assessed in English from a university on our list of approved universities in non-majority English speaking countries (non-MESC).

If you are not a national of a majority English speaking country, then your degree must be no more than five years old* at the beginning of your programme of study. (*Revised 05 March 2024 to extend degree validity to five years.)

Find out more about our language requirements:

Academic Technology Approval Scheme

If you are not an EU, EEA or Swiss national, you may need an Academic Technology Approval Scheme clearance certificate in order to study this programme.

AwardTitleDurationStudy mode
PhDArtificial Intelligence for Biomedical Innovation4 YearsFull-timeTuition fees
PhDArtificial Intelligence for Biomedical Innovation6 YearsPart-timeTuition fees

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  • Informatics Graduate School
  • 10 Crichton Street
  • Central Campus
  • Edinburgh
  • EH8 9AB
Round Application deadline
1 26 April 2024

You must submit two references with your application.

You must submit an application via the EUCLID application portal and provide the required information and documentation.

This will include submission of:

  • a Curriculum Vitae (CV)
  • a covering letter describing the applicants motivations, interests, and suitability for the CDT programme.
  • degree certificates and official transcripts of all completed and in-progress degrees (plus certified translations if academic documents are not issued in English).
  • two academic references

Only complete applications will progress forward to the academic selection stage.

Read through detailed guidance on how to apply for a PGR programme in the School of Informatics:

Application guidance is also available at

Find out more about the general application process for postgraduate programmes:

Further information

  • Informatics Graduate School
  • 10 Crichton Street
  • Central Campus
  • Edinburgh
  • EH8 9AB